Daniel Langr

771 total citations
46 papers, 494 citations indexed

About

Daniel Langr is a scholar working on Hardware and Architecture, Nuclear and High Energy Physics and Computer Networks and Communications. According to data from OpenAlex, Daniel Langr has authored 46 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Hardware and Architecture, 20 papers in Nuclear and High Energy Physics and 18 papers in Computer Networks and Communications. Recurrent topics in Daniel Langr's work include Parallel Computing and Optimization Techniques (22 papers), Nuclear physics research studies (18 papers) and Quantum Chromodynamics and Particle Interactions (17 papers). Daniel Langr is often cited by papers focused on Parallel Computing and Optimization Techniques (22 papers), Nuclear physics research studies (18 papers) and Quantum Chromodynamics and Particle Interactions (17 papers). Daniel Langr collaborates with scholars based in Czechia, United States and Japan. Daniel Langr's co-authors include Pavel Tvrdı́k, T. Dytrych, Kristina D. Launey, J. P. Draayer, James P. Vary, Pieter Maris, Érik Saule, Masha Sosonkina, Ümit V. Çatalyürek and M. A. Caprio and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Physics Letters B.

In The Last Decade

Daniel Langr

43 papers receiving 487 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Langr Czechia 12 283 147 137 119 113 46 494
Hai Ah Nam United States 9 258 0.9× 16 0.1× 145 1.1× 39 0.3× 57 0.5× 21 389
A. G. Sibiryakov Russia 5 73 0.3× 127 0.9× 65 0.5× 75 0.6× 46 0.4× 6 278
M. E. Sevior Australia 15 589 2.1× 10 0.1× 117 0.9× 61 0.5× 36 0.3× 70 769
Ronald Babich United States 10 597 2.1× 54 0.4× 38 0.3× 60 0.5× 10 0.1× 15 725
S. J. Goldsack United Kingdom 11 181 0.6× 58 0.4× 34 0.2× 66 0.6× 8 0.1× 59 381
A. S. Ito Japan 16 1.1k 4.0× 54 0.4× 60 0.4× 14 0.1× 7 0.1× 80 1.4k
R. van Dantzig Netherlands 12 265 0.9× 79 0.5× 95 0.7× 196 1.6× 9 0.1× 45 542
Venera Khoromskaia Germany 13 18 0.1× 48 0.3× 207 1.5× 12 0.1× 110 1.0× 31 546
Atsushi Kawai Japan 12 14 0.0× 126 0.9× 55 0.4× 97 0.8× 7 0.1× 25 398
Germán Rodrigo Spain 27 1.8k 6.4× 30 0.2× 106 0.8× 24 0.2× 12 0.1× 78 2.0k

Countries citing papers authored by Daniel Langr

Since Specialization
Citations

This map shows the geographic impact of Daniel Langr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Langr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Langr more than expected).

Fields of papers citing papers by Daniel Langr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Langr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Langr. The network helps show where Daniel Langr may publish in the future.

Co-authorship network of co-authors of Daniel Langr

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Langr. A scholar is included among the top collaborators of Daniel Langr based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Langr. Daniel Langr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yoshida, K., Kazuyuki Ogata, Kristina D. Launey, et al.. (2025). Ab initio informed 20Ne(p, pα)16O reaction elucidates the emergence of alpha clustering from chiral potentials. Physics Letters B. 866. 139563–139563.
2.
Baker, Robert, et al.. (2025). Response functions and giant monopole resonances for light to medium-mass nuclei from the ab initio symmetry-adapted no-core–shell model. Journal of Physics G Nuclear and Particle Physics. 52(3). 35107–35107. 1 indexed citations
3.
Draayer, J. P., et al.. (2024). Coupling and recoupling coefficients for Wigner’s U(4) supermultiplet symmetry. The European Physical Journal Plus. 139(10). 1 indexed citations
4.
Launey, Kristina D., et al.. (2024). Uncertainty quantification of collective nuclear observables from the chiral potential parametrization. Physica Scripta. 99(12). 125311–125311. 1 indexed citations
5.
Langr, Daniel & T. Dytrych. (2023). Parallel multithreaded deduplication of data sequences in nuclear structure calculations. The International Journal of High Performance Computing Applications. 38(1). 5–16.
6.
Sargsyan, G. H., Kristina D. Launey, M. T. Burkey, et al.. (2022). Impact of Clustering on the Li8 β Decay and Recoil Form Factors. Physical Review Letters. 128(20). 202503–202503. 16 indexed citations
7.
Langr, Daniel, et al.. (2016). Block Iterators for Sparse Matrices. SHILAP Revista de lepidopterología. 8. 695–704. 4 indexed citations
8.
Langr, Daniel, et al.. (2016). Efficient parallel evaluation of block properties of sparse matrices. SHILAP Revista de lepidopterología. 8. 709–716. 1 indexed citations
9.
Dytrych, T., A. C. Hayes, Kristina D. Launey, et al.. (2015). Electron-scattering form factors forLi6in theab initiosymmetry-guided framework. Physical Review C. 91(2). 16 indexed citations
10.
Rohlíček, Ján, et al.. (2015). A new parallel and GPU version of aTREOR-based algorithm for indexing powder diffraction data. Journal of Applied Crystallography. 48(1). 166–170. 4 indexed citations
11.
Langr, Daniel, Pavel Tvrdı́k, T. Dytrych, & J. P. Draayer. (2014). Algorithm 947. ACM Transactions on Mathematical Software. 41(1). 1–26. 6 indexed citations
12.
Langr, Daniel, et al.. (2014). Downsampling Algorithms for Large Sparse Matrices. International Journal of Parallel Programming. 43(5). 679–702. 1 indexed citations
13.
Langr, Daniel, et al.. (2014). Tree-based Space Efficient Formats for Storing the Structure of Sparse Matrices. Scalable Computing Practice and Experience. 15(1). 5 indexed citations
14.
Langr, Daniel, et al.. (2014). Large-Scale Visualization of Sparse Matrices. Scalable Computing Practice and Experience. 15(1). 1 indexed citations
15.
Langr, Daniel, et al.. (2013). Storing sparse matrices to files in the adaptive-blocking hierarchical storage format. Federated Conference on Computer Science and Information Systems. 479–486. 6 indexed citations
16.
Dytrych, T., Kristina D. Launey, J. P. Draayer, et al.. (2013). Collective Modes in Light Nuclei from First Principles. Physical Review Letters. 111(25). 252501–252501. 95 indexed citations
17.
Launey, Kristina D., T. Dytrych, J. P. Draayer, et al.. (2013). Symmetry-adaptedab initiono-core shell model calculations for12C. Journal of Physics Conference Series. 436. 12023–12023. 1 indexed citations
18.
Langr, Daniel, et al.. (2012). Adaptive-blocking hierarchical storage format for sparse matrices. Civil War Book Review. 545–551. 13 indexed citations
19.
Langr, Daniel, et al.. (2012). Minimal Quadtree Format for Compression of Sparse Matrices Storage. 359–364. 12 indexed citations
20.
Draayer, J. P., T. Dytrych, Kristina D. Launey, & Daniel Langr. (2012). Symmetry-adapted no-core shell model applications for light nuclei with QCD-inspired interactions. Progress in Particle and Nuclear Physics. 67(2). 516–520. 17 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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